使用
使用
- 提高事实准确性：通过以真实世界的信息为依据生成 回答，减少模型产生幻觉的情况。
- 获取实时信息：回答有关近期事件 和主题的问题。
- 提供来源：通过显示模型声明的来源，赢得用户信任或允许用户浏览 相关网站。
- 完成更复杂的任务：检索工件和相关图片、 视频或其他媒体，以协助完成推理任务。
- 改进特定于区域或语言的回答：查找特定于区域的 信息，或协助准确翻译内容。
支持的模型
gemini-3.1-pro-previewgemini-3.5-flashgemini-3.1-flash-litegemini-3-pro-image-preview（又称“Nano Banana Pro”）gemini-3.1-flash-image-preview（又称“Nano Banana 2”）gemini-2.5-progemini-2.5-flashgemini-2.5-flash-lite
支持的语言
请参阅 支持的语言，了解 Gemini 模型。
使用 Google Search 接地
|
点击您的 Gemini API 提供商，以查看此页面上特定于提供商的内容 和代码。 |
创建 GenerativeModel 实例时，请提供 GoogleSearch 作为模型可用于生成回答的 tool。
Swift
import FirebaseAILogic
// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())
// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(
modelName: "GEMINI_MODEL_NAME",
// Provide Google Search as a tool that the model can use to generate its response.
tools: [Tool.googleSearch()]
)
let response = try await model.generateContent("Who won the euro 2024?")
print(response.text ?? "No text in response.")
// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result
Kotlin
// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
modelName = "GEMINI_MODEL_NAME",
// Provide Google Search as a tool that the model can use to generate its response
tools = listOf(Tool.googleSearch())
)
val response = model.generateContent("Who won the euro 2024?")
print(response.text)
// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result
Java
// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
.generativeModel("GEMINI_MODEL_NAME",
null,
null,
// Provide Google Search as a tool that the model can use to generate its response
List.of(Tool.GoogleSearch()));
// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);
ListenableFuture response = model.generateContent("Who won the euro 2024?");
Futures.addCallback(response, new FutureCallback() {
@Override
public void onSuccess(GenerateContentResponse result) {
String resultText = result.getText();
System.out.println(resultText);
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result
Web
import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";
// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
// ...
};
// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);
// Initialize the Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });
// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(
ai,
{
model: "GEMINI_MODEL_NAME",
// Provide Google Search as a tool that the model can use to generate its response
tools: [{ googleSearch: {} }]
}
);
const result = await model.generateContent("Who won the euro 2024?");
console.log(result.response.text());
// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result
Dart
import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_ai/firebase_ai.dart';
import 'firebase_options.dart';
// Initialize FirebaseApp.
await Firebase.initializeApp(
options: DefaultFirebaseOptions.currentPlatform,
);
// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model = FirebaseAI.googleAI().generativeModel(
model: 'GEMINI_MODEL_NAME',
// Provide Google Search as a tool that the model can use to generate its response.
tools: [
Tool.googleSearch(),
],
);
final response = await model.generateContent([Content.text("Who won the euro 2024?")]);
print(response.text);
// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result
Unity
using Firebase;
using Firebase.AI;
// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());
// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(
modelName: "GEMINI_MODEL_NAME",
// Provide Google Search as a tool that the model can use to generate its response.
tools: new[] { new Tool(new GoogleSearch()) }
);
var response = await model.GenerateContentAsync("Who won the euro 2024?");
UnityEngine.Debug.Log(response.Text ?? "No text in response.");
// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result
了解如何选择适合您的用例和应用的模型 （可选）。
使用 Google Search 接地的工作原理
使用 GoogleSearch 工具时，模型会自动处理搜索、处理和引用信息的整个工作流程。
模型的工作流程如下：
- 接收提示：您的应用会向启 2/}模型
用
GoogleSearch工具的 Gemini 模型发送提示。Gemini - 分析提示：模型会分析提示，并确定
Google Search 是否可以改进其回答。 - 向
Google Search 发送查询：如果需要，模型 会自动生成一个或多个搜索查询并执行这些查询。 - 处理搜索结果：模型会处理
Google Search 结果，并针对原始 提示生成回答。 - 返回“接地结果”：模型会返回最终的、用户友好的
回答，该回答以
Google Search 结果为依据。此回答包含模型的文本回答和groundingMetadata，其中包含搜索查询、Web 结果和来源。
请注意，即使您向模型提供 groundingMetadata 对象，因此不属于“接地结果”。
了解接地结果
如果模型以 groundingMetadata 对象，其中包含结构化数据
，这些数据对于验证声明和在应用中打造丰富的来源体验至关重要。
“接地结果”中的 groundingMetadata 对象包含以下信息：
webSearchQueries：发送给Google Search 的搜索查询数组。此信息有助于调试和了解模型的推理过程。searchEntryPoint：包含用于呈现所需的 “Google Search 建议”的 HTML 和 CSS。您必须遵守所选 API 提供方（Gemini Developer API 或 Vertex AI Gemini API）的“使用Google Search 接地”的使用要求（请参阅服务专用条款中的 服务条款 (TOS) 部分）。本页面的后面部分会介绍如何 使用和显示接地结果 。groundingChunks：包含 Web 来源（uri和title）的对象数组。groundingSupports：用于将模型回答text连接到groundingChunks中的来源的块数组。每个块会将文本segment（由startIndex和endIndex定义）链接到一个或多个groundingChunkIndices。此字段有助于您构建内嵌来源链接。 本页面的后面部分会介绍如何 使用和显示接地结果 。
以下是包含 groundingMetadata 对象的回答示例：
{
"candidates": [
{
"content": {
"parts": [
{
"text": "Spain won Euro 2024, defeating England 2-1 in the final. This victory marks Spain's record fourth European Championship title."
}
],
"role": "model"
},
"groundingMetadata": {
"webSearchQueries": [
"UEFA Euro 2024 winner",
"who won euro 2024"
],
"searchEntryPoint": {
"renderedContent": "<!-- HTML and CSS for the search widget -->"
},
"groundingChunks": [
{"web": {"uri": "https://vertexaisearch.cloud.google.com.....", "title": "aljazeera.com"}},
{"web": {"uri": "https://vertexaisearch.cloud.google.com.....", "title": "uefa.com"}}
],
"groundingSupports": [
{
"segment": {"startIndex": 0, "endIndex": 85, "text": "Spain won Euro 2024, defeatin..."},
"groundingChunkIndices": [0]
},
{
"segment": {"startIndex": 86, "endIndex": 210, "text": "This victory marks Spain's..."},
"groundingChunkIndices": [0, 1]
}
]
}
}
]
}
使用和显示接地结果
如果模型使用 groundingMetadata 对象 in the
response。
您必须显示 建议
除了遵守使用
（必需） 显示 Google Search 建议
如果回答包含“
groundingMetadata 对象包含“searchEntryPoint 字段，该字段具有 renderedContent 字段，该字段提供合规的 HTML 和 CSS 样式，您需要实现这些样式以在应用中显示搜索建议。
请查看
有关
请参阅本部分后面的示例代码示例 。
@media(prefers-color-scheme)（必需）显示来源
groundingMetadata 对象包含结构化来源数据，具体来说是 groundingSupports 和 groundingChunks 字段。使用此信息将模型的声明直接链接到界面中的来源（内嵌和汇总）。
请参阅本部分后面的示例代码示例 。
示例代码示例
这些代码示例提供了使用和显示接地结果的通用模式。 不过，您有责任确保您的具体实现符合合规性要求。
Swift
// ...
// Get the model's response
let text = response.text
// Get the grounding metadata
if let candidate = response.candidates.first,
let groundingMetadata = candidate.groundingMetadata {
// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
if let renderedContent = groundingMetadata.searchEntryPoint?.renderedContent {
// TODO(developer): Display Google Search suggestions using a WebView
}
// REQUIRED - display sources
let groundingChunks = groundingMetadata.groundingChunks
for chunk in groundingMetadata.groundingChunks {
if let web = chunk.web {
let title = web.title // for example, "uefa.com"
let uri = web.uri // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show source in the UI
}
}
}
Kotlin
// ...
// Get the model's response
val text = response.text
// Get the grounding metadata
val groundingMetadata = response.candidates.firstOrNull()?.groundingMetadata
// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
val renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent
if (renderedContent != null) {
// TODO(developer): Display Google Search suggestions using a WebView
}
// REQUIRED - display sources
val groundingChunks = groundingMetadata?.groundingChunks
groundingChunks?.let { chunks ->
for (chunk in chunks) {
val title = chunk.web?.title // for example, "uefa.com"
val uri = chunk.web?.uri // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show source in the UI
}
}
Java
// ...
Futures.addCallback(response, new FutureCallback() {
@Override
public void onSuccess(GenerateContentResponse result) {
// Get the model's response
String text = result.getText();
// Get the grounding metadata
GroundingMetadata groundingMetadata =
result.getCandidates()[0].getGroundingMetadata();
if (groundingMetadata != null) {
// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
String renderedContent =
groundingMetadata.getSearchEntryPoint().getRenderedContent();
if (renderedContent != null) {
// TODO(developer): Display Google Search suggestions using a WebView
}
// REQUIRED - display sources
List chunks = groundingMetadata.getGroundingChunks();
if (chunks != null) {
for(GroundingChunk chunk : chunks) {
WebGroundingChunk web = chunk.getWeb();
if (web != null) {
String title = web.getTitle(); // for example, "uefa.com"
String uri = web.getUri(); // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show sources in the UI
}
}
}
}
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
Web
// ...
// Get the model's text response
const text = result.response.text();
// Get the grounding metadata
const groundingMetadata = result.response.candidates?.[0]?.groundingMetadata;
// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
const renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent) {
// TODO(developer): render this HTML and CSS in the UI
}
// REQUIRED - display sources
const groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks) {
for (const chunk of groundingChunks) {
const title = chunk.web?.title; // for example, "uefa.com"
const uri = chunk.web?.uri; // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show sources in the UI
}
}
Dart
// ...
// Get the model's response
final text = response.text;
// Get the grounding metadata
final groundingMetadata = response.candidates.first.groundingMetadata;
// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
final renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent != null) {
// TODO(developer): Display Google Search suggestions using a WebView
}
// REQUIRED - display sources
final groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks != null) {
for (var chunk in groundingChunks) {
final title = chunk.web?.title; // for example, "uefa.com"
final uri = chunk.web?.uri; // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show sources in the UI
}
}
Unity
// ...
// Get the model's response
var text = response.Text;
// Get the grounding metadata
var groundingMetadata = response.Candidates.First().GroundingMetadata.Value;
// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
if (groundingMetadata.SearchEntryPoint.HasValue) {
var renderedContent = groundingMetadata.SearchEntryPoint.Value.RenderedContent;
// TODO(developer): Display Google Search suggestions using a WebView
}
// REQUIRED - display sources
foreach(GroundingChunk chunk in groundingMetadata.GroundingChunks) {
var title = chunk.Web.Value.Title; // for example, "uefa.com"
var uri = chunk.Web.Value.Uri; // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show sources in the UI
}
Firebase 控制台中的接地结果和 AI 监控
如果您已在 控制台中Firebase启用 AI 监控，则 回答会存储在Cloud Logging中。默认情况下，此数据的保留期限为 30 天。
您有责任确保此保留期限或您设置的任何自定义 期限与您的具体用例以及所选 Gemini API 提供商的任何其他 合规性要求完全一致： Gemini Developer API 或 Vertex AI Gemini API（请参阅 服务条款 部分，该部分位于服务专用条款中）。您可能需要 调整保留期限，以Cloud Logging 满足这些要求。
价格和限制
请务必查看所选 Gemini API 提供商文档中有关使用